




"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a_        =       logging.get_logger(__name__)

a_        =       {'vocab_file': 'sentencepiece.bpe.model'}

a_        =       {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

a_        =       {
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

a_        =       '▁'

class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=VOCAB_FILES_NAMES
		UpperCamelCase							=PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase							=["input_ids", "attention_mask"]
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_="<s>"				,						UpperCamelCase_="</s>"				,						UpperCamelCase_="</s>"				,						UpperCamelCase_="<s>"				,						UpperCamelCase_="<unk>"				,						UpperCamelCase_="<pad>"				,						UpperCamelCase_="<mask>"				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       None:
					# Mask token behave like a normal word, i.e. include the space before it
					__lowercase			:  Tuple								=       AddedToken(UpperCamelCase_				,						lstrip=UpperCamelCase_				,						rstrip=UpperCamelCase_       ) if isinstance(UpperCamelCase_				,						UpperCamelCase_       ) else mask_token

					__lowercase			:  Union[str, Any]								=       {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=UpperCamelCase_				,						eos_token=UpperCamelCase_				,						unk_token=UpperCamelCase_				,						sep_token=UpperCamelCase_				,						cls_token=UpperCamelCase_				,						pad_token=UpperCamelCase_				,						mask_token=UpperCamelCase_				,						sp_model_kwargs=self.sp_model_kwargs				,						**UpperCamelCase_				,						)

					__lowercase			:  Optional[Any]								=       vocab_file
					__lowercase			:  str								=       spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(str(UpperCamelCase_       )       )

					__lowercase			:  List[Any]								=       {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

					__lowercase			:  str								=       len(self.sp_model       ) - 1
					__lowercase			:  str								=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_ = None       )						->       List[int]:

					if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__lowercase			:  int								=       [self.cls_token_id]
					__lowercase			:  List[str]								=       [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						UpperCamelCase_ = False       )						->       List[int]:
					if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=UpperCamelCase_				,						token_ids_a=UpperCamelCase_				,						already_has_special_tokens=UpperCamelCase_       )

					if token_ids_a is None:
								return [1] + ([0] * len(UpperCamelCase_       )) + [1]
					return [1] + ([0] * len(UpperCamelCase_       )) + [1, 1] + ([0] * len(UpperCamelCase_       )) + [1]
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_ = None       )						->       List[int]:
					__lowercase			:  Any								=       [self.sep_token_id]
					__lowercase			:  Union[str, Any]								=       [self.cls_token_id]

					if token_ids_a is None:
								return len(cls + token_ids_a + sep       ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
		@property
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					return len(self.sp_model       )
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					__lowercase			:  List[str]								=       {self.convert_ids_to_tokens(UpperCamelCase_       ): i for i in range(self.vocab_size       )}
					vocab.update(self.added_tokens_encoder       )
					return vocab
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       List[str]:
					return self.sp_model.encode(UpperCamelCase_				,						out_type=UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       Optional[Any]:
					if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
					__lowercase			:  Optional[Any]								=       self.sp_model.PieceToId(UpperCamelCase_       )

					return spm_id if spm_id else self.unk_token_id
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       str:
					if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       int:
					__lowercase			:  Union[str, Any]								=       []
					__lowercase			:  int								=       ''''''
					__lowercase			:  int								=       False
					for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
											if not prev_is_special:
														out_string += " "
											out_string += self.sp_model.decode(UpperCamelCase_       ) + token
											__lowercase			:  List[str]								=       True
											__lowercase			:  Optional[int]								=       []
								else:
											current_sub_tokens.append(UpperCamelCase_       )
											__lowercase			:  int								=       False
					out_string += self.sp_model.decode(UpperCamelCase_       )
					return out_string.strip()
		def __getstate__(						self       )						->       Any:
					__lowercase			:  Optional[Any]								=       self.__dict__.copy()
					__lowercase			:  Optional[int]								=       None
					return state
		def __setstate__(						self				,						UpperCamelCase_       )						->       Tuple:
					__lowercase			:  str								=       d

					# for backward compatibility
					if not hasattr(self				,						'''sp_model_kwargs'''       ):
								__lowercase			:  Any								=       {}

					__lowercase			:  Dict								=       spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(self.vocab_file       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_ = None       )						->       Tuple[str]:
					if not os.path.isdir(UpperCamelCase_       ):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
								return
					__lowercase			:  Tuple								=       os.path.join(
					    UpperCamelCase_				,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

					if os.path.abspath(self.vocab_file       ) != os.path.abspath(UpperCamelCase_       ) and os.path.isfile(self.vocab_file       ):
								copyfile(self.vocab_file				,						UpperCamelCase_       )
					elif not os.path.isfile(self.vocab_file       ):
								with open(UpperCamelCase_				,						'''wb'''       ) as fi:
											__lowercase			:  Any								=       self.sp_model.serialized_model_proto()
											fi.write(UpperCamelCase_       )

					return (out_vocab_file,)










"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						lowercase   (   unittest.TestCase   ):





      @property
      def        _snake_case     (			self					)						->	List[str]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			UNetaDModel(
                block_out_channels=(32, 64) ,		layers_per_block=2 ,		sample_size=32 ,		in_channels=3 ,		out_channels=3 ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,		)
            return model





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			self.dummy_uncond_unet
            lowerCAmelCase    		=			ScoreSdeVeScheduler()

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase ,		return_dict=lowercase					)[
                0
            ]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase    		=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""google/ncsnpp-church-256"""
            lowerCAmelCase    		=			UNetaDModel.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVeScheduler.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=10 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase    		=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






